Entropy-based techniques for creation of well-balanced computer based reasoning systems

ABSTRACT

Techniques are provided herein for creating well-balanced computer-based reasoning systems and using those to control systems. The techniques include receiving a request to determine whether to include one or more particular data elements in a computer-based reasoning model and determining two probability density or mass functions (“PDMFs”), one for the data set including the one or more particular data elements, once for the data set excluding it. Surprisal is determined based on those two PDMFs, and inclusion in the computer-based reasoning model is determined based on surprisal. A system is later controlled using the computer-based reasoning model.

FIELD OF THE INVENTION

The present invention relates to computer-based optimization andartificial intelligence techniques and in particular to entropy-basedtechniques for creation of balanced computer-based reasoning systems.

BACKGROUND

One of the hardest parts of using computer-based reasoning systems issimultaneously obtaining sufficient breadth of training data whilereducing model size, as those two goals are often at odds. Dataelements, possibly including context data paired with action data (e.g.,a set of one or more contexts and/or a set of one or more actions),which may include ‘cases’ or ‘instances’ in the case of case-basedreasoning, can be collected for many points in time and for manydecisions made and actions taken in many contexts. For example, if atrainer is driving a vehicle to train a self-driving vehicle,context-action pairs may be collected every second or even multipletimes a second, and those context-action pairs may represent, forexample, driving actions taken (e.g., change lanes, turn, etc.) inparticular contexts (e.g., vehicle speed, weight, location, proximity toother objects, etc.). Further, sets of context-action pairs may becollected multiple times per trainer (e.g., a single trainer driving avehicle multiple times) and there may be many trainers (e.g., differentdrivers contributing training data). In total, the training dataelements may number in the millions, billions, or even higher. This, inturn, increases the size of the computer-based reasoning model. While alarger computer-based reasoning model is useful for coverage, the largerthe model is, the more computing resources are used to control a systemwith the model. So, although good breadth in the model is useful, theincreasing size of the computer-based reasoning model can be adetriment. Further, a computer-based reasoning model may have morefeatures (e.g., data elements used in the context of a context-actionpair) and may not use proper parameters (e.g., feature weights, etc.).Each of these issues can cause inefficiencies in the model and its use.

The techniques herein address these issues by using entropy-basedtechniques to balance the need for smaller computer-based reasoningmodels with the usefulness of broad coverage.

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

SUMMARY

The claims may serve as a summary of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 depicts a process for creation of well-balanced computer-basedreasoning systems.

FIG. 2 depicts a block diagram of a system for creation of well-balancedcomputer-based reasoning systems.

FIG. 3 depicts additional example systems and hardware for creation ofwell-balanced computer-based reasoning systems.

FIG. 4 depicts an example process for controlling a system.

FIG. 5 depicts additional example processes for creation ofwell-balanced computer-based reasoning systems.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to avoid unnecessarily obscuring thepresent invention.

General Overview

As noted above, one of the hardest parts of using computer-basedreasoning systems is simultaneously obtaining sufficient breadth oftraining data while reducing model size, as those two goals are often atodds. The need for broad coverage pushes the size of sets of dataelements higher. Stated another way, a training set needs to have thegood coverage in order for it to be useful later in a computer-basedreasoning system. As such, trainers need to cover a wide range ofcontexts in order to ensure that the needed coverage is obtained.Collecting data for this broad coverage causes the size of the sets ofdata elements to increase.

Having such large amounts of data can be useful for providing choice ofactions to take in many contexts, but has downsides. Large sets of dataelements take significant memory to store and incur significantprocessing costs when later finding matching context-action pairs. Assuch, it is important to do one or both of: 1) reducing the number ofdata elements during or after collection and 2) directing training sothat when a contextual area is already well covered, training can bedirected to areas where training data will provide a greater differencein the amount of information contained in the model or set of dataelements.

Techniques herein address these issues, including obtaining broadcoverage while still controlling the size of the set of data elementsfor a computer-based reasoning model while still providing broadcoverage in the model.

Various embodiments herein look at the amount of new information thateach data element provides to the overall set of data elements in orderto determine whether to include (or keep) that data element. In someways, looking at the information contributed may be considered lookingat whether the new data is “useful” to the new set of data elements, orwhether the new data is “surprising” or informative based on the set ofdata elements. Various embodiments herein use a measure of informationentropy to determine the additional surprisal (or surprise) that a datapoint provides to a set of data. Information entropy is the expectedvalue of surprisal. Example measures of surprisal are describedelsewhere herein.

Information gain can be applied across the spectrum of machine learningapplications for computer-based reasoning models including “supervisedlearning” and “unsupervised learning”. In supervised learning, acomputer-based reasoning model may contain a number of training caseswith a set of inputs, sometimes called a feature vector or context, anda set of outputs, sometimes called labels, decisions, or actions. Thefeature vectors are the inputs observed and the labels are thepresumably correct decisions for the given inputs as given by thetrainer. In many implementations, the feature vectors and labels areeach comprised of a set of numbers, but in other implementations, thefeature vector and labels may each include enumerations, alphanumericstrings, or other data. In unsupervised learning, a computer-basedreasoning model contains no outputs, labels, or actions in the trainingcases, and it is up to the machine learning system and the model todetermine how to label the cases. However, a model, available trainingdata, and other experimental, live, validated, unvalidated, test, orother available data may contain a combination of labeled and unlabeleddata, as well as data that contains different feature vectors anddifferent kinds of actions or labels. As long as some function isdefined that can relate two particular cases that may include featurevectors or labels, all of the techniques herein may be applied to anyset of feature vectors and labels for supervised or unsupervisedlearning.

The use of information entropy can be used to help reduce the number ofdata elements in a set after it has been collected, while maintainingmost of the overall breadth or usefulness that set of data elements. Forexample, a set of data elements related to vehicle operation (e.g., frommultiple training runs by multiple trainers) can be large andcumbersome. Some embodiments herein calculate the expected surprisal, orinformation gain, of each of the data elements in the set of dataelements and remove those that contribute little to the overallinformational value of the set of data (e.g., those with low surprisal).Some embodiments calculate the information gain of each data element inthe set of data elements and only keep those with the highest surprisal(e.g., the top N surprisal data elements and/or those with aninformation gain value over a certain threshold). Some embodiments maycalculate the information gain of each data element in the set of dataelements and only keep those with the lowest surprisal, identifying andreporting those with the highest surprisal as anomalous results.

As noted above, surprisal can be used to reduce the size of sets of dataelements as they are collected, while controlling the total size of theset of data elements. In some embodiments, training data collectedduring training runs is analyzed in real time or near real time and isonly stored to a set of data elements if it adds significantly to theinformation for that set of data elements. The surprisal of each dataelement may be determined, and when the surprisal is above a certain(lower limit) threshold (or “within bounds” of that lower limitthreshold), it may be added to the set of training data. For example,using the self-driving car example, a data element related to drivingstraight on a highway at a constant speed might have a low surprisalvalue due to plentiful relevant training data, and therefore not beadded to the set of data elements. Data elements related to driving intraffic in the rain, however, may have high surprisal value due to lessrelevant training data, and therefore be added to the set of dataelements.

In some embodiments, surprisal is used to help direct training. Dataelements with high surprisal are flagged and trainers may be directed totrain more around those areas. Trainers may also be signaled whensurprisal is low, indicating that more training is not needed in thatarea. As training is occurring, the contribution of new data elementscan be calculated in real time or near real time, and if the surprisalvalue is high (e.g., above a certain lower limit threshold), the trainermay be notified that additional training data in this context may beneeded. If the surprisal is low, then the driver may be signaled thatthe current context is not providing much information, indicating thatthe trainer should move on to a different context or to demonstrate anyunusual actions that may result from similar contexts. For example,using the self-driving car example, if training in a current context(e.g., driving at a constant speed on a highway) does not provide muchadditional information to the set of data elements (e.g., the new dataelements have low surprisal), the driver may be given information thatthe current context is not providing much information and a differentcontext (e.g., side street driving is needed). If the data elements inthe current context are providing much additional information (e.g.,have high surprisal), the trainer may be signaled to continue to providetraining data in this context. For example, if, in a set of dataelements related to vehicle operation there is only a single dataelement related to traversing a railroad track, that data element mayhave a very high surprisal value and may therefore be flagged so thattrainers may know to provide more training data related to railroadtracks.

When errors or anomalies are detected in a set of data elements, the“offending” data element(s) may be removed and or corrected. This can beespecially important when the data element had low surprisal (which maybe interpreted as a high confidence answer). As such, in someembodiments, when there are errors or anomalies detected with dataelements that have low surprisal, those elements may be removed and/ormore elements may be added related to the offending data element. Ananomalous case with high surprisal may also be removed upon detection.When a data element with high surprisal produces an anomalous result, itis less extraordinary than when a data element with low surprisalproduces an anomalous result. Nevertheless, taking corrective actionwhen a data element with high surprisal produces an anomalous result mayalso benefit the model.

Because information gain measures the surprisal of one distribution toanother, information gain can be used to assist in the process offeature selection. Feature selection is the process of determining whichfeatures, contexts, data values, etc. should be considered in order toarrive at an appropriate label or decision. Feature selection is animportant problem in machine learning and data science because too manyfeatures or presence of irrelevant features can result in problemsincluding slower training, increased memory usage, decrease accuracy,and decreased performance, but often it is hard to know which featuresare important. The information gain may be computed for each featurefrom the associated probability density function of the model without afeature relative to the model with the feature. By assessing theinformation gain of each feature, features with the least informationgain can be removed with the least negative impact to the performance ofthe model because they have the least effect on the structure of thedata set and the results returned. Conversely, features with the highestinformation gain can be evaluated to see if they are improving ordiminishing accuracy by comparing the results of the model with andwithout those high entropy features.

In some embodiments, information gain can be used to tune parameters toa computer-based reasoning system. Parameters may include proximity,similarity, topology, feature weights, data transformations, functionselection, etc. Given a base configuration of model parameters, otherparameter choices or combinations of choices may be evaluated withregard to information gain relative to the base configuration (e.g., bycalculating a PDMF using each candidate configuration). Thoseparameterizations with higher information gain will expose morecomplexity of the domain of the feature vector. This configuration withhigher information gain may yield better performance, and it mayindicate or reveal problems with the features or the selection offeatures.

In some embodiments, information gain can be used to compare twodifferent training models to determine which model has more or lesspredictable complex behavior relative to the other one.

Information gain can be computed as a rate based on new training datathat is being put into the computer-based reasoning model. As the modelbecomes more trained in the domain, the information gain of new trainingdata is expected to drop, and each new piece of training data will yieldless. However, an increased rate of information gain means that themodel is learning new things; a significant or sustained high rate ofinformation gain may be used to trigger a model optimization to removedata that may now be less informative.

In some embodiments, as described elsewhere herein, relative surprisalis calculated using

-   -   log₂ (P/Q), where P is the posterior probability of an event        occurring after it has occurred divided by the prior        probability, Q, of that same event occurring before it has        occurred.

In some embodiments, different measures that are correlated with,related to, or share similar characteristics of information entropy maybe used. Although the accuracy, performance, precision, domains, andranges may be applicable or invalid in different circumstances, otherfunctions may include variance, Gini coefficient, mean absolutedifference, median absolute deviation, variance-to-mean ratio, otherdispersion methods, and other techniques for finding differences betweenprobability density or probability mass functions.

In some embodiments, the surprisal is calculated from the probabilitydensity or mass functions (PDMFs) on the hypervolumes of the contextsrepresented by the multidimensional space of the set of data elementsand performing analytical or numerical methods of Bayesian inferencesusing the PDMFs. Further, the embodiments may use appropriate PDMFestimation techniques on the data elements, such as multivariate normal,gaussian, Laplace, radial, quadratic, logistic, sigmoid, cosine,tricubic, quartic, parabolic, maximal entropy, other parametric ornonparametric distributions, or different kernel density estimation orapproximation techniques for each data element or subset of dataelements in the set of data elements before the data element or dataelements are added (Q) and then again after they are added (P).

In some embodiments, the surprisal of a data element with respect to aset of data elements can be calculated based on the probability thateach element will be within the kth nearest elements to a given point,where the probability of being among the kth nearest elements iscalculated using a set of distance measures on a generalized spanningtree that represents the topology of the set of data elements based ontheir k nearest neighbors. The surprisal of a data element with respectto a set of data elements may be calculated using three probabilitydensity or mass functions. For example, consider the three PDMFs (inthis case probability mass functions):

P(i)=DistContrib(particular data element i)/Σ DistContrib(eachparticular data element in the set of data elements)

Q_(known)(i)=DistContrib(particular data element i)/Σ DistContrib(eachparticular data element in the set of data elements & expected value ofelements previously unknown),

Q_(unknown)(i)=Average (DistContrib(each data element in the set of dataelements))/Σ DistContrib(each particular data element & set of dataelements),

and if each data element is weighted identically, Q_(unknown) may be1/N, where N is the number of data elements. Q_(known) refers to dataelements that were known prior to their inclusion in P, and Q_(known)refers to the data elements that were unknown and assumed as expectationprior to their inclusion in P. The shorthand (or function)DistContrib(X) may be a measure, premetric, or other function of thenearest neighbors to X. An example calculation is:

DistContrib(X)=ΣC_(i)Distance(nearest neighbor_(i)),

where C_(i) is a coefficient and nearest_neighbor_(i) is the i^(th)nearest neighbor of data element X, and i=1. . . N for a DistContribcalculation of the N nearest neighbors.

The nearest neighbors and the distance calculation may be determinedusing any appropriate distance measurement or other premetric, includingEuclidean distance, Minkowski distance, Damerau-Levenshtein distance,Kullback-Leibler divergence, 1-Kronecker delta, and/or any otherdistance measure, metric, pseudometric, premetric, index, and the like.The list of coefficients may be any appropriate list, such as adecreasing series including the harmonic series (1/i) and other serieslike (1/(i+1)), (N−i+1), (N²−i²+1), (1/i²), etc., a constant number(e.g., C_(i)=1), an increasing series (e.g., C_(i)=i), or anon-monotonic series (e.g., C_(i)=sin (i*pi/7)).

The techniques discussed herein, in some embodiments, can be used tocompare two or more models or parts of two or more models. Thiscomparison can be useful for summarizing differences between the modelsand for determining whether models are good candidates for combiningand/or using evolutionary programming techniques. Further, thetechniques herein are useful to case-based reasoning systems (one typeof computer-based reasoning), but are also useful for data and modelreduction for machine learning and artificial intelligence systems (alsotypes of computer-based reasoning systems). For those system, trainingdata can become excessive, and training and retraining the neuralnetwork can be time and computationally intensive. Reducing the size ofthe training sets can be beneficial for reducing training data (amongother benefits) while minimizing the loss of information in thetraining.

Overview of Surprisal, Entropy, and Divergence

Below is a brief summary of some concepts discussed herein. It will beappreciated that there are numerous ways to compute the concepts below,and that other, similar mathematical concepts can be used with thetechniques discussed herein.

Entropy (“H(x)”) is a measure of the average expected value ofinformation from an event and is often calculated as the sum overobservations of the probability of each observation multiple by thenegative log of the probability of the observation.

H(x)=−Σip(x _(i))* log p(x _(i))

Entropy is generally considered a measure of disorder. Therefore, highervalues of entropy represent less regularly ordered information, withrandom noise having high entropy, and lower values of entropy representmore ordered information, with a long sequence of zeros having lowentropy. If log₂ is used, then entropy may be seen as representing thetheoretical lower bound on the number of bits needed to represent theinformation in a set of observations. Entropy can also be seen as howmuch a new observation distorts the combined PDMF of the observed space.Consider, for example, a universe of observations where there is acertain probability that each of A, B, or C occurs, and a probabilitythat something other than A, B, or C occurs.

Surprisal (“I(x)”) is a measure of how much information is provided by anew event x_(i).

I(x _(i))=−log p(x _(i))

Surprisal is generally a measure of surprise (or new information)generated by an event. The smaller the probability of X_(i), the higherthe surprisal.

Kullback-Leibler Divergence (“KL divergence” or “Div_(KL)(x)”) is ameasure of difference in information between two sets of observation. Itis often represented as

Div_(KL)(x)=Σi p(x _(i))*(log p(x _(i))−log q(x _(i))),

-   -   where p(xi) is the probability of x_(i) after x_(i) has        occurred, and q(xi) is the probability of x_(i) before x_(i) has        occurred.

Example Processes for Entropy-Based Techniques for Creation ofWell-Balanced Computer Based Reasoning Systems

FIG. 1 depicts a process for using entropy-based techniques for creationof well-balanced computer-based reasoning system. As an overview, in theprocess 100 of FIG. 1, a request is received 110 to determine whether toinclude a particular data element (or one or more data elements) in thecomputer-based reasoning model. The receipt 110 of this request could bepart of reduction (in size, memory used, etc.) of an existingcomputer-based reasoning model, adding training data to a model, and thelike. After receiving the request on whether to include the data elementor elements in the computer-based reasoning model, the process willdetermine 120 and 130 two PDMFs, one for the set of data elementsassociated with the computer-based reasoning model without the one ormore particular data elements calculating expected values for futuredata elements, and one for the full set of data elements, including theone or more particular data elements. The surprisal is then determined140 based on the two PDMFs, and a decision is made whether to include150 the one or more particular data elements in the computer-basedreasoning model based on the surprisal. The process 100 may optionallybe repeated for multiple data elements or groups of data elements(indicated by the dashed line in FIG. 1). Once the data element(s) areincluded or excluded from the computer-based reasoning model, areal-world system may be controlled 160 with the computer-basedreasoning model (such as an autonomous vehicle, an image labelingsystem, etc.).

Returning to the top of FIG. 1, the process receives 110 a request todetermine whether to include particular data in a computer-basedreasoning model. The received 110 request may be a request to reduce thesize of a computer-based reasoning model. For example, a system ordevice (not depicted in FIG. 2), may request the reduction in model sizefor a computer-based reasoning model to the training and analysis system210. In other embodiments, the training and analysis system 210 mayinitiate the model reduction request on its own (e.g., when a modelreaches a certain threshold or at a fixed interval). In someembodiments, the request received 110 can be to reduce the model to aparticular size, by a certain amount, or based on the informationalvalue of the elements of the model (described more herein). As describedherein, reducing the size of the computer-based reasoning model whilemaintaining most of the informational value of the model is beneficial.The model being culled could be any appropriate model, includingcomputer-based reasoning models for self-driving vehicles, labellingimages, decisions on claims (e.g., how to fund a claim based on thefactors of the case), and the like.

In some embodiments, the request to determine whether to include the oneor more particular data elements in a computer-based reasoning model isreceived 110 as part of training. For example, if the training isongoing, the request received 110 may be a request to determine whetherto add a newly-received data element to the computer-based reasoningmodel. As a particular vehicular example, if Alicia is training aself-driving car computer-based reasoning system, and data(context-action pairs) is being collected for that drive (perhaps inreal time, perhaps after the fact, but before the data is added to themodel), then process 100 may be used to determine whether each elementof data for Alicia's training data should be added to the computer-basedreasoning model. Determining whether to add the elements before they areadded to the computer-based reasoning model will allow the model tomaintain a smaller size (by not adding elements that do not providesufficient informational value), while still adding those elements thatdo provide informational value. As discussed herein, having a smallermodel with high informational content is beneficial.

A first PDMF is determined 120 for the set of data elements thatexcludes the one or more particular data elements, and a second PDMF isdetermined 130 for the set of data elements that includes the one ormore particular data elements. In some embodiments, as discussed herein,the determination of whether to include data in a computer-basedreasoning model is made as part of a model reduction. In suchembodiments, a PDMF is determined 130 for the model as it currentlystands (e.g., with the data element in question) and another isdetermined 120 for the computer-based reasoning model excluding the dataelement. For example, if a determination is being made whether one ormore particular data elements (e.g., a context-action pair) should beincluded/remain in the computer-based reasoning model, then a PDMF forthe computer-based reasoning model with the data element will bedetermined as well one without that data element using placeholderexpected values for the data. These two PDMFs will be used to determinewhether to keep the data element in the computer-based reasoning model.In some embodiments, the second PDMF may be calculated based on treatingthe model as an ‘empty model’ where the probability of every dataelement is the interpreted as the same or “even”, instead of usingexisting data element probability densities.

In some embodiments, the determination of whether to include one or moreparticular data elements in a computer-based reasoning model happensbefore data is added to the computer-based reasoning model. When thedetermination is being made whether to add a data element to acomputer-based reasoning model, a PDMF is determined for the model as itstands (e.g., without the one or more particular data elements, using anexpected value instead) and another is determined for the model with thedata element added. These two PDMFs will be used to determine whether toadd the data element to the computer-based reasoning model.

The calculation of a PDMF is discussed elsewhere herein in detail. Insome embodiments, determining 120 and/or 130 a PDMF includes using amultivariate Laplace distribution, a multivariate Gaussian distribution,numerical methods of Bayesian inference, or other kernel methods.

In some embodiments, determining 120 and/or 130 a PDMF includesdetermining multiple nearest data elements from the set of data elementsin the computer-based reasoning model for the one or more particulardata elements, and the distance contribution for each. A combineddistance measure is then determined for the one or more particular dataelements based on the distance measures for the nearest-neighborelements' distances (as described elsewhere, these can be equallyweighted, harmonically weighted, etc.), and the PDMF can be determinedbased at least in part on the combined distance measure.

Surprisal is determined 140 based on the first and second PDMFs. Forexample, in some embodiments, the surprisal of the one or moreparticular data elements is the ratio of the first and second PDMFs.Determination of surprisal is discussed extensively herein. As noted, insome embodiments, the surprisal is a calculation of P/Q. Otherembodiments include different calculations for surprisal. For example,surprisal could be calculated as log (P)/log (Q), (P*log (P))/(Q*log(Q)), PA2/QA2, X*P/Q (where X is a coefficient), Q/P, etc. Theembodiments discussed primarily herein are those in which P (or afunction thereof) is in the numerator and Q (or a function thereof) arein the denominator, but the techniques apply equally even if thepositions of P and Q are swapped. In the embodiments where P is in thenumerator of the equation and Q is in the denominator, higher surprisalcan be associated with the one or more particular data elementsproviding more information to the model; and lower surprisal can beassociated with the one or more particular data elements providing lessinformation to the model. The opposite could be true when P is in thedenominator and Q is in the numerator. The higher the informationprovided to the model from the data element, the “better” the model willbe with the data element included. Therefore, the higher the surprisal,the more likely the data element will be added to the model.

Process 100 then proceeds by determining whether to include 150 the oneor more particular data elements based on the determined 140 surprisal.As noted above and elsewhere, the higher the surprisal of the one ormore particular data elements, the more information it provides to themodel, and the more likely it should be included in the model. In someembodiments, determining whether to include 150 the one or moreparticular data elements in the model includes determining whether thesurprisal is above a (lower limit) threshold. If the surprisal of a newdata element meets the particular threshold, then it will be included inthe model. This approach can be useful when the goal of using thetechniques herein is to balance information in the model and model size(whether pruning an existing model or building a model as data elementsare considered, e.g., during training). In some embodiments, thesurprisal threshold is a numeric threshold (e.g., 0.1, 1, 2.1, 100,etc.). The surprisal is then compared to that threshold in order to makethe determination of whether to include 150 the one or more particulardata elements. In some embodiments, the surprisal threshold is a ratioof the surprisal of the one or more particular data elements and theaverage surprisal of the data elements of the computer-based reasoningmodel. For example, if the one or more data elements has a surprisalthat is X% (e.g., 100%, 150%, 200%, etc.) of the average surprisal ofthe computer-based reasoning model, then it may be included in thecomputer-based reasoning model. It may be beneficial to not add cases tothe model with low entropy when it would not provide sufficientadditional information to the computer-based reasoning model. Forexample, a low pass filter may remove anomalies, and a high pass filtermay remove redundancies. So, in some embodiments, the surprisal iscompared both to high and low thresholds, and is only added if thesurprisal is within the bounds (or not outside the bounds) of the twothresholds.

In some embodiments, the element with the top N surprisals are the onlyones included in the computer-based reasoning model. Limiting the modelto a certain number (N) of data elements may be a useful approach when acertain limit on the computer-based reasoning model size is desired forreasons such as memory availability, tolerable latency for the model torespond, and computational effort required. In examples and embodimentsin which a reduction in computer-based reasoning model of a particularsize is the goal (e.g., removing D data elements), then the dataelements with the lowest N surprisal may be excluded from the model.

Consider the example of Alicia training a self-driving vehiclesimulation. As the new data elements (e.g., context-action pairs relatedto the context of the vehicle and the actions being taken) are received,each may be assessed for surprisal with respect to the computer-basedreasoning model being built. If the goal is to limit the addition of newdata elements to only those with certain surprisal, then the surprisalmay be compared to a threshold, and the data element may only be addedto the computer-based reasoning model if the surprisal for the dataelement exceeds a (lower limit) threshold. If the goal is to limit thecomputer-based reasoning model size to a particular threshold, then allcandidate data elements may be assessed, and only those with the highestsurprisal are added to the computer-based reasoning model (e.g., thedata elements with the top N surprisals, where N is the goal for thenumber of data elements in the computer-based reasoning model).

Going further into the example, surprising data elements (those withhigh surprisal) may be those that are least related to previous dataelements in the computer-based reasoning model. For example, if Aliciahas not previously driven over railroad tracks, then data elements(e.g., context-action pairs) related to actions taken in the context ofdriving over railroad tracks may be the most surprising. If Alicia hasdriven for many miles on straight stretches of highway during daylight,then additional data elements in that context may not generate highsurprisal scores.

As another example, some embodiments are related to systems for labelingimages. Human experts may label images in order to identify features ofthe images and/or the subject of the image. These labels, and thecontexts in which they were made (the image being the primary source ofthe context), may be used as training data for a computer-basedreasoning model. The techniques herein could be used to determine howmuch surprisal each new data element (e.g., a context-label pair)provides, and only include those data elements that have a surprisalabove a certain (lower limit) threshold. Similarly, a computer-basedreasoning model for image labeling could also be pruned, assessing eachdata element and including only the data elements with the top Nsurprisals and/or excluding the data elements with the bottom Dsurprisals.

As yet another example, some embodiments relate to making decisions onhow to value claims. For example, numerous input data may be gatheredrelated to a claim (data on the entity or person making the claim, howand when the underlying event occurred, etc.). As new data elements forclaim valuation are received, each can have its surprisal determinedrelative to the existing computer-based reasoning model. Those new dataelements with surprisals above a certain threshold would be added to thecomputer-based reasoning model. Those with surprisals below thethreshold may be excluded from the computer-based reasoning model.Further, the computer-based reasoning model may be pruned by excludingthe data elements with the lowest surprisal and/or only including thosewith the highest surprisal.

As alluded to in the examples above, in some embodiments, more than oneembodiment or approach described herein may be used (not depicted inFIG. 1). For example, during the training of a computer-based reasoningsystem, only data elements with surprisals above a particular thresholdmay be added to the computer-based reasoning model. Once the training isover, it may be pruned (e.g., limiting the model to the top N most“surprising” data elements and/or removing the bottom D least surprisingdata elements). Further, in some embodiments, the criteria used foradding (or pruning) may change over time. For example, the threshold toadd new data elements to a computer-based reasoning model may increaseas the model grows, making it yet harder for a data element to be“surprising” enough to be added to the model. Additionally, or in thealternative, the threshold to add new data elements may decrease overtime, allowing data elements to be added even if they are lesssurprising. Further, the threshold may stay the same and, due to thedecreased relative informativeness of data elements in the same trainingdomain, fewer data elements will be accepted into the model as the modelbecomes asymptotically representative of the training domain. In thisway, the techniques recognize that, as a computer-based reasoning modelgrows, it becomes increasingly difficult for new data elements to be“surprising.”

As depicted in FIG. 1, the process 100 may optionally return todetermine whether other data elements should be included in thecomputer-based reasoning model (e.g., indicated by the dashed line from150 to 110). In the embodiments and examples in which a model is beingbuilt (e.g., during training), this includes new data elements beingconsidered for inclusion. For example, as Alicia is driving, new dataelements, such as context-action pairs can be assessed for inclusion inthe computer-based reasoning model using the techniques herein. In thecontext of reducing model size once it has been built, the process 100may be run for each element (or some subset of them) in thecomputer-based reasoning model. As noted elsewhere herein, the dataelements of an existing computer-based reasoning model may be assesseduntil a threshold number (D) have been excluded from the computer-basedreasoning model and/or a threshold number (N) have been selected forinclusion in the computer-based reasoning model.

In some embodiments, when the determined 140 surprisal is below acertain threshold, the techniques may include flagging that thesurprisal is low (not depicted in FIG. 1). This can be useful, forexample, during collection of training data. For example, if Alicia isdriving in a context where much data has already been collected (e.g.,daytime highway driving and straight sections of road), and thesurprisal for the data elements in those contexts could be low. As such,Alicia could be given an indication (e.g., in the form of an audio cuefrom a computer-based reasoning training and analysis system 210 withinthe vehicle, or the like) that driving in the current context was notproviding much additional information to the computer-based reasoningmodel. In response to the flagging, Alicia might exit the highway tostart training the computer-based reasoning on side streets. Techniquesand embodiments such as this not only help control the size of thecomputer-based reasoning model but also could be helpful in reducing theamount of time and effort needed to train the computer-based reasoningmodel by helping focus the training. Further, an indication thatincoming data elements are not providing much additional information canalso be an indication that the computer-based reasoning model is ripefor pruning and such an indication could be used to prompt the start ofprocess 100.

In some embodiments, another way a model may be culled is by removingdata elements associated with anomalous actions (not depicted in FIG.1). An anomaly could be flagged during later operation (e.g., if ananomalous action occurs, it could be flagged by an operator of thesystem being controlled). In some embodiments, the context-action pairor data element associated with the anomalous action could be flaggedfor removal. The anomalous data element could be removed from the model.Removing anomalous data not only can benefit the use of the modelbecause anomalous decision will no longer (or less likely) be made usingthe computer-based reasoning model, but also the computer-basedreasoning model will be smaller, which has the benefits discussedherein.

When an anomaly is detected, more data “around” the data elementassociated with the anomaly might be needed. For example, if an anomalyis detected, the context in which the anomaly occurred might be ripe foradditional data elements. This could be “flagged” for a trainer, whocould then focus training on that context. These additional dataelements could then be considered for addition to the computer-basedreasoning model in the manner described herein.

When the model is ready for use it may be provided to a control system(e.g., control system 220 of FIG. 2) for control of a real-world system.One example of controlling a system is controlling an image labellingsystem which is discussed with respect to FIG. 4, and elsewhere herein.

Another example of controlling a real-world system is controlling aself-driving vehicle. Vehicle-related data elements and control arediscussed with respect to FIG. 4 and elsewhere herein, and can includeobtaining contextual data for a current context for the self-drivingvehicle (e.g., what context is the vehicle in at the moment),determining an action based on the current context, and causingperformance of the determined context for the self-driving vehicle.

Additional Example Process for Entropy-Based Techniques for Creation ofWell-Balanced Computer Based Reasoning Systems

The techniques herein are often described in terms of including orexcluding particular data elements, such as data context-action pairs,as part of, e.g., a case-based reasoning model. In some embodiments, inaddition to or instead of including particular context action pairs, thetechniques can be used to include or exclude other types of dataelements, such as features of data elements a computer-based reasoningmodel and/or parameters of a computer-based reasoning model. Forexample, the techniques can be used to determine the surprisal offeatures in the data elements. As one example and turning to process 500of FIG. 5, in the vehicular context, the data elements may include aninput features, such as road width on which the vehicle is driving. Thesurprisal for the inclusion of road width can be determined 520, 530,540. And the determination whether to select or include 550 the featurecan then be made. After that, the vehicle could be controlled 560 usingthe updated computer-based reasoning model. Further, this can be donefor features that are inputs (e.g., road width, vehicle weight, etc.),as well as outputs (e.g., whether to break, turn left, etc.). As anotherexample, the techniques herein may include determining whether toinclude or exclude particular parameters of the computer-based reasoningmodel, such as proximity, similarity, topology, feature weights, datatransformations, function selection, etc. used in the computer-basedreasoning model.

Turning to the top of FIG. 5, a request may be received 510 as towhether to include or select one or more particular aspects in acomputer-based reasoning model. As noted above, these aspects can befeatures of data elements (e.g., individual or sets of values orvariables in the contexts, particular action data, etc.). The aspectscan also be aspects of the computer-based reasoning model itself, suchas proximity, similarity, topology, feature weights, datatransformations, function selection, etc.

PDMFs are determined 520 and 530 for the model with and without theparticular aspects of the computer-based reasoning model, and thesurprisal of including the particular aspects can be determined 540 fromthe two PDMFs. Determining PDMFs are described elsewhere herein. In thevehicular example, a determination could be made for the computer-basedreasoning model including in the list of features considered the widthof the road (for the first PDMF) and without the width of the road (thesecond PDMF). If the surprisal determined is above a certain (lowerlimit) threshold (e.g., a numeric value or a percentage as compared tothe average for the computer-based reasoning model), then the featuremay be selected or included 550 in the computer-based reasoning model,or, e.g., the feature of road width may be considered in the dataelements in the model. It may be beneficial to not add cases to themodel with low entropy when it would not provide sufficient additionalinformation to the computer-based reasoning model, and to avoid addingcases with very high surprisal to avoid adding anomalous cases. Forexample, a low pass filter may remove anomalies, and a high pass filtermay remove redundancies. So, in some embodiments, the surprisal iscompared both to high and low thresholds, and is only added if thesurprisal is within the bounds (not out of bounds) of the twothresholds.

As another example, a request may be received 510 to determine whichdistance function (e.g., Euclidean distance, Minkowski distance,Damerau-Levenshtein distance, Kullback-Leibler divergence, etc.) andwhich distance function parameters to use for calculating distance amongdata elements. The surprisal can be determined 520, 530, 540 for each ofthe candidate premetrics/distance measures and the function with thehighest surprisal may be chosen as the parameter to be selected orincluded 550 with the computer-based reasoning model.

Process 500 optionally may return from the determination whether toselect or include 550 particular aspects into the computer-basedreasoning in order to receive more requests 510, and make moredetermination 520-550 of what to include in the computer-based reasoningmodel. When there are no more aspects to consider selecting or including550, the computer-based reasoning model may be sent to a control systemand a system may be controlled 560 with that computer-based reasoningmodel. Various aspects of controlling the system are discussedthroughout herein, including with respect to FIG. 4.

As used herein, the term “model elements” is a broad term encompassingit plain and ordinary meaning and includes data elements (definedelsewhere herein) and aspects of computer-based reasoning models(defined elsewhere herein). As such, any discussion herein of thetechniques with respect to either the data elements or the aspects ofcomputer-based reasoning models would also be applicable to modelelements of the computer-based reasoning model.

Comparing Two Computer Based Reasoning Systems

In some embodiments, the techniques herein include comparing twocomputer-based reasoning models to see which of the two is moresurprising and/or has more information. For example, the data elements(e.g., using process 100) or aspects (e.g., using process 500) of onecomputer-based reasoning model can be compared to another computer-basedreasoning model. The model with the higher surprisal would be consideredto have more information. This determination can be useful when themodels differ (possibly even considerably), and a determination on whichmodel provides more information will inform a choice of which model touse. Further, one computer-based reasoning model can be directlycompared to one or more computer-based reasoning models by computing thesurprisal of adding all of the training elements contained in the firstcomputer-based reasoning model to each of the others. The surprisal ofeach pairing indicates which models are anomalous compared to thebaseline. Individual training cases can be compared from onecomputer-based reasoning model to another, and the highest surprisaltraining cases show where the first model differs from the second.

Example Processes for Controlling Systems

FIG. 4 depicts an example process 400 for controlling a system. In someembodiments and at a high level, the process 400 proceeds by receivingor receiving 410 a computer-based reasoning model for controlling thesystem. The computer-based reasoning model may be one created usingprocess 100, as one example. In some embodiments, the process 400proceeds by receiving 420 a current context for the system, determining430 an action to take based on the current context and thecomputer-based reasoning model, and causing 440 performance of thedetermined action (e.g., labelling an image, causing a vehicle toperform the turn, lane change, waypoint navigation, etc.). If operationof the system continues 450, then the process returns to receive 420 thecurrent context, and otherwise discontinues 460 control of the system.

As discussed herein the various processes 100, 400, 500, etc. may run inparallel, in conjunction, together, or one process may be a subprocessof another. Further, any of the processes may run on the systems orhardware discussed herein. The features and steps of processes 100, 400,and 500 could be used in combination and/or in different orders.

Self-Driving Vehicles

Returning to the top of the process 400, it begins by receiving 410 acomputer-based reasoning model for controlling the system. Thecomputer-based reasoning model may be received in any appropriatematter. It may be provided via a network 290, placed in a shared oraccessible memory on either the training and analysis system 210 orcontrol system 220, or in accessible storage, such as storage 230 or240.

In some embodiments (not depicted in FIG. 4), an operational situationcould be indicated for the system. The operational situation is relatedto context, but may be considered a higher level, and may not change (orchange less frequently) during operation of the system. For example, inthe context of control of a vehicle, the operational situation may beindicated by a passenger or operator of the vehicle, by a configurationfile, a setting, and/or the like. For example, a passenger Alicia mayselect “drive like Alicia” in order to have the vehicle driver like her.As another example, a fleet of helicopters may have a configuration fileset to operate like Bob. In some embodiments, the operational situationmay be detected. For example, the vehicle may detect that it isoperating in a particular location (area, city, region, state, orcountry), time of day, weather condition, etc. and the vehicle may beindicated to drive in a manner appropriate for that operationalsituation.

The operational situation, whether detected, indicated by passenger,etc., may be changed during operation of the vehicle. For example, apassenger may first indicate that she would like the vehicle to drivecautiously (e.g., like Alicia), and then realize that she is runninglater and switch to a faster operation mode (e.g., like Carole). Theoperational situation may also change based on detection. For example,if a vehicle is operating under an operational situation for aparticular portion of road, and detects that it has left that portion ofroad, it may automatically switch to an operational situationappropriate for its location (e.g., for that city), may revert to adefault operation (e.g., a baseline program that operates the vehicle)or operational situation (e.g., the last used). In some embodiments, ifthe vehicle detects that it needs to change operational situations, itmay prompt a passenger or operator to choose a new operationalsituation.

In some embodiments, the computer-based reasoning model is receivedbefore process 400 begins (not depicted in FIG. 4), and the processbegins by receiving 420 the current context. For example, thecomputer-based reasoning model may already be loaded into a controller220 and the process 400 begins by receiving 420 the current context forthe system being controlled. In some embodiments, referring to FIG. 2,the current context for a system to be controlled (not depicted in FIG.2) may be sent to control system 220 and control system 220 may receive420 current context for the system.

Receiving 420 current context may include receiving the context dataneeded for a determination to be made using the computer-based reasoningmodel. For example, turning to the vehicular example, receiving 420 thecurrent context may, in various embodiments, include receivinginformation from sensors on or near the vehicle, determining informationbased on location or other sensor information, accessing data about thevehicle or location, etc. For example, the vehicle may have numeroussensors related to the vehicle and its operation, such as one or more ofeach of the following: speed sensors, tire pressure monitors, fuelgauges, compasses, global positioning systems (GPS), RADARs, LiDARs,cameras, barometers, thermal sensors, accelerometers, strain gauges,noise/sound measurement systems, etc. Current context may also includeinformation determined based on sensor data. For example, the time toimpact with the closest object may be determined based on distancecalculations from RADAR or LiDAR data, and/or may be determined based ondepth-from-stereo information from cameras on the vehicle. Context mayinclude characteristics of the sensors, such as the distance a RADAR orLiDAR is capable of detecting, resolution and focal length of thecameras, etc. Context may include information about the vehicle not froma sensor. For example, the weight of the vehicle, acceleration,deceleration, and turning or maneuverability information may be knownfor the vehicle and may be part of the context information.Additionally, context may include information about the location,including road condition, wind direction and strength, weather,visibility, traffic data, road layout, etc.

Referring back to the example of vehicle control rules for Bob flying ahelicopter, the context data for a later flight of the helicopter usingthe vehicle control rules based on Bob's operation of the helicopter mayinclude fuel remaining, distance that fuel can allow the helicopter totravel, location including elevation, wind speed and direction,visibility, location and type of sensors as well as the sensor data,time to impact with the N closest objects, maneuverability and speedcontrol information, etc. Returning to the stop sign example, whetherusing vehicle control rules based on Alicia or Carole, the context mayinclude LiDAR, RADAR, camera and other sensor data, locationinformation, weight of the vehicle, road condition and weatherinformation, braking information for the vehicle, etc.

The control system then determined 430 an action to take based on thecurrent context and the computer-based reasoning model. For example,turning to the vehicular example, an action to take is determined 430based on the current context and the vehicle control rules for thecurrent operational situation. In some embodiments that use machinelearning, the vehicle control rules may be in the form of a neuralnetwork (as described elsewhere herein), and the context may be fed intothe neural network to determine an action to take. In embodiments usingcase-based reasoning, the set of context-action pairs closest to thecurrent context may be determined. In some embodiments, only the closestcontext-action pair is determined, and the action associated with thatcontext-action pair is the determined 430 action. In some embodiments,multiple context-action pairs are determined 430. For example, the N“closest” context-action pairs may be determined 430, and either as partof the determining 430, or later as part of the causing 440 performanceof the action, choices may be made on the action to take based on the Nclosest context-action pairs, where “distance” for between the currentcontext can be measured using any appropriate technique, including useof Euclidean distance, Minkowski distance, Damerau-Levenshtein distance,Kullback-Leibler divergence, and/or any other distance measure, metric,psuedometric, premetric, index, or the like.

In some embodiments, the actions to be taken may be blended based on theaction of each context-action pair, with invalid (e.g., impossible ordangerous) outcomes being discarded. A choice can also be made among theN context-action pairs chosen based on criteria such as choosing to usethe same or different operator context-action pair from the lastdetermined action. For example, in an embodiment where there arecontext-action pair sets from multiple operators in the vehicle controlrules, the choice of which context-action pair may be based on whether acontext-action pair from the same operator was just chosen (e.g., tomaintain consistency). The choice among the top N context-action pairsmay also be made by choosing at random, mixing portions of the actionstogether, choosing based on a voting mechanism, etc.

Some embodiments include detecting gaps in the training data and/orvehicle control rules and indicating those during operation of thevehicle (for example, via prompt and/or spoken or graphical userinterface) or offline (for example, in a report, on a graphical display,etc.) to indicate what additional training is needed (not depicted inFIG. 4). In some embodiments, when the computer-based reasoning systemdoes not find context “close enough” to the current context to make aconfident decision on an action to take, it may indicate this andsuggest that an operator might take manual control of the vehicle, andthat operation of the vehicle may provide additional context and actiondata for the computer-based reasoning system. Additionally, in someembodiments, an operator may indicate to a vehicle that she would liketo take manual control to either override the computer-based reasoningsystem or replace the training data. These two scenarios may differ bywhether the data (for example, context-action pairs) for the operationalscenario are ignored for this time period, or whether they are replaced.

In some embodiments, the operational situation may be chosen based on aconfidence measure indicating confidence in candidate actions to takefrom two (or more) different sets of control rules (not depicted in FIG.4). Consider a first operational situation associated with a first setof vehicle control rules (e.g., with significant training from Aliciadriving on highways) and a second operational situation associated witha second set of vehicle control rules (e.g., with significant trainingfrom Carole driving on rural roads). Candidate actions and associatedconfidences may be determined for each of the sets of vehicle controlrules based on the context. The determined 430 action to take may thenbe selected as the action associated with the higher confidence level.For example, when the vehicle is driving on the highway, the actionsfrom the vehicle control rules associated with Alicia may have a higherconfidence, and therefore be chosen. When the vehicle is on rural roads,the actions from the vehicle control rules associated with Carole mayhave higher confidence and therefore be chosen. Relatedly, in someembodiments, a set of vehicle control rules may be hierarchical, andactions to take may be propagated from lower levels in the hierarchy tohigh levels, and the choice among actions to take propagated from thelower levels may be made on confidence associated with each of thosechosen actions. The confidence can be based on any appropriateconfidence calculation including, in some embodiments, determining howmuch “extra information” in the vehicle control rules is associated withthat action in that context.

In some embodiments, there may be a background or baseline operationalprogram that is used when the computer-based reasoning system does nothave sufficient data to make a decision on what action to take (notdepicted in FIG. 4). For example, if in a set of vehicle control rules,there is no matching context or there is not a matching context that isclose enough to the current context, then the background program may beused. If none of the training data from Alicia included what to do whencrossing railroad tracks, and railroad tracks are encountered in lateroperation of the vehicle, then the system may fall back on the baselineoperational program to handle the traversal of the railroad tracks. Insome embodiments, the baseline model is a computer-based reasoningsystem, in which case context-action pairs from the baseline model maybe removed when new training data is added. In some embodiments, thebaseline model is an executive driving engine which takes over controlof the vehicle operation when there are no matching contexts in thevehicle control rules (e.g., in the case of a context-based reasoningsystem, there might be no context-action pairs that are sufficiently“close”).

In some embodiments, determining 430 an action to take based on thecontext can include determining whether vehicle maintenance is needed.As described elsewhere herein, the context may include wear and/ortiming related to components of the vehicle, and a message related tomaintenance may be determined based on the wear or timing. The messagemay indicate that maintenance may be needed or recommended (e.g.,because preventative maintenance is often performed in the timing orwear context, because issues have been reported or detected withcomponents in the timing or wear context, etc.). The message may be sentto or displayed for a vehicle operator (such as a fleet managementservice) and/or a passenger. For example, in the context of anautomobile with sixty thousand miles, the message sent to a fleetmaintenance system may include an indication that a timing belt may needto be replaced in order to avoid a P percent chance that the belt willbreak in the next five thousand miles (where the predictive informationmay be based on previously-collected context and action data, asdescribed elsewhere herein). When the automobile reaches ninety thousandmiles and assuming the belt has not been changed, the message mayinclude that the chance that the belt will break has increased to, e.g.,P*4 in the next five thousand miles.

Performance of the determined 430 action is then caused 440. Turning tothe vehicular example, causing 440 performance of the action may includedirect control of the vehicle and/or sending a message to a system,device, or interface that can control the vehicle. The action sent tocontrol the vehicle may also be translated before it is used to controlthe vehicle. For example, the action determined 430 may be to navigateto a particular waypoint. In such an embodiment, causing 440 performanceof the action may include sending the waypoint to a navigation system,and the navigation system may then, in turn, control the vehicle on afiner-grained level. In other embodiments, the determined 430 action maybe to switch lanes, and that instruction may be sent to a control systemthat would enable the car to change the lane as directed. In yet otherembodiments, the action determined 430 may be lower-level (e.g.,accelerate or decelerate, turn 4° to the left, etc.), and causing 440performance of the action may include sending the action to be performedto a control of the vehicle, or controlling the vehicle directly. Insome embodiments, causing 440 performance of the action includes sendingone or more messages for interpretation and/or display. In someembodiments, the causing 440 the action includes indicating the actionto be taken at one or more levels of a control hierarchy for a vehicle.Examples of control hierarchies are given elsewhere herein.

Some embodiments include detecting anomalous actions taken or caused 440to be taken. These anomalous actions may be signaled by an operator orpassenger, or may be detected after operation of the vehicle (e.g., byreviewing log files, external reports, etc.). For example, a passengerof a vehicle may indicate that an undesirable maneuver was made by thevehicle (e.g., turning left from the right lane of a 2-lane road) or logfiles may be reviewed if the vehicle was in an accident. Once theanomaly is detected, the portion of the vehicle control rules (e.g.,context-action pair(s)) related to the anomalous action can bedetermined. If it is determined that the context-action pair(s) areresponsible for the anomalous action, then those context-action pairscan be removed or replaced using the techniques herein.

Referring to the example of the helicopter fleet and the vehicle controlrules associated with Bob, the vehicle control 220 may determine 430what action to take for the helicopter based on the received 420context. The vehicle control 220 may then cause the helicopter toperform the determined action, for example, by sending instructionsrelated to the action to the appropriate controls in the helicopter. Inthe driving example, the vehicle control 220 may determine 430 whataction to take based on the context of vehicle. The vehicle control maythen cause 440 performance of the determined 430 action by theautomobile by sending instructions to control elements on the vehicle.

If there are more 450 contexts for which to determine actions for theoperation of the system, then the process 400 returns to receive 420more current contexts. Otherwise, process 400 ceases 460 control of thesystem. Turning to the vehicular example, as long as there is acontinuation of operation of the vehicle using the vehicle controlrules, the process 400 returns to receive 420 the subsequent currentcontext for the vehicle. If the operational situation changes (e.g., theautomobile is no longer on the stretch of road associated with theoperational situation, a passenger indicates a new operationalsituation, etc.), then the process returns to determine the newoperational situation. If the vehicle is no longer operating undervehicle control rules (e.g., it arrived at its destination, a passengertook over manual control, etc.), then the process 400 will discontinue460 autonomous control of the vehicle.

Many of the examples discussed herein for vehicles discuss self-drivingautomobiles. As depicted in FIG. 2, numerous types of vehicles can becontrolled. For example, a helicopter 251 or drone, a submarine 252, orboat or freight ship 253, or any other type of vehicle such as plane ordrone (not depicted in FIG. 2), construction equipment, (not depicted inFIG. 2), and/or the like. In each case, the computer-based reasoningmodel may differ, including using different features, using differenttechniques described herein, etc. Further, the context of each type ofvehicle may differ. Flying vehicles may need context data such asweight, lift, drag, fuel remaining, distance remaining given fuel,windspeed, visibility, etc. Floating vehicles, such as boats, freightvessels, submarines, and the like may have context data such asbuoyancy, drag, propulsion capabilities, speed of currents, a measure ofthe choppiness of the water, fuel remaining, distance capabilityremaining given fuel, and the like. Manufacturing and other equipmentmay have as context width of area traversing, turn radius of thevehicle, speed capabilities, towing/lifting capabilities, and the like.

Image Labelling

The process 100 or 500 may also be applied in the context of animage-labeling system. For example, numerous experts may label images(e.g., identifying features of or elements within those images). Forexample, the human experts may identify cancerous masses on x-rays.Having these experts label all input images is incredibly time consumingto do on an ongoing basis, in addition to being expensive (paying theexperts). The techniques herein may be used to train an image-labelingcomputer-based reasoning model based on previously-trained images. Oncethe image-labeling computer-based reasoning system has been built, theninput images may be analyzed using the image-based reasoning system. Inorder to build the image-labeling computer-based reasoning system,images may be labeled by experts and used as training data. Using thetechniques herein, the surprisal of the training data can be used tobuild an image-labeling computer-based reasoning system that balancesthe size of the computer-based reasoning model with the information thateach additional image (or set of images) with associated labelsprovides. Once the image-labelling computer-based reasoning is trained,it can be used to label images in the future. For example, a new imagemay come in, the image-labelling computer-based reasoning may determineone or more labels for the image, and then the one or more labels maythen be applied to the image. Thus, these images can be labeledautomatically, saving the time and expense related to having expertslabel the images.

In some embodiments, process 100 or 500 may determine (e.g., based on arequest 110, 510) the surprisal of each image (or multiple images) andthe associated labels or of the aspects of the computer-based reasoningmodel. For each one or more images and their labels, a first and secondPDMF may be determined 120, 130, 520, 530 (determining the PDMF isdescribed elsewhere herein). The surprisal for the one or more imagesmay be determined 140, 540 and a determination may be made whether toselect or include 150, 550 the one or more images (or aspects) in theimage-labeling computer-based reasoning model based on the determinedsurprisal. While there are more sets of one or more images with labelsto assess, the process 100 or 500 may return to determine whether moreimage or label sets should be included or whether aspects should beincluded and/or changed in the model. Once there are no more images oraspects to consider, the process 100 or 500 can turn to controlling 160,560 the image analysis system using the image-labeling computer-basedreasoning.

Controlling 160, 560 an image-labeling system may be accomplished byprocess 400. For example, if the data elements are related to images andlabels applied to those images, then the image-labeling computer-basedreasoning model trained on that data will apply labels to incomingimages. Process 400 proceeds by receiving 410 an image-labelingcomputer-based reasoning model. The process proceeds by receiving 420 animage for labeling. The image-labeling computer-based reasoning model isthen used to determine 430 labels for the input image. The image is thenlabeled 440. If there are more 450 images to label, then the systemreturns to receive 420 those images and otherwise ceases 460. In suchembodiments, the image-labeling computer-based reasoning model may beused to select labels based on which training image is “closest” to theincoming image. The label(s) associated with that image will then beselected to apply to the incoming image.

Manufacturing and Assembly

The process 100 or 500 may also be applied in the context ofmanufacturing and/or assembly. For example, entropy can be used toidentify normal behavior versus anomalous behavior of such equipment.Using the techniques herein, a crane (e.g., crane 255 of FIG. 2), robotarm, or other actuator is attempting to “grab” something and itssurprisal is too high, it can stop, sound an alarm, shutdown certainareas of the facility, and/or request for human assistance. Anomalousbehavior that is detected via entropy among sensors and actuators can beused to detect when there is some sort breakdown, unusual wear and tearor mechanical or other malfunction, an unusual component or seed orcrop, etc. It can also be used to find damaged equipment for repairs orbuffing or other improvements for any robots that are searching andcorrecting defects in products or themselves (e.g., fixing a broken wireor smoothing out cuts made to the ends of a manufactured artifact madevia an extrusion process). Entropy can also be used for cranes and othergrabbing devices to find which cargo or items are closest matches towhat is needed. Entropy can be used to drastically reduce the amount oftime to train a robot to perform a new task for a new product or customorder, because the robot will indicate the aspects of the process itdoes not understand and direct training towards those areas and awayfrom things it has already learned. Combining this with stopping ongoingactions when an anomalous situation is detected would also allow a robotto begin performing work before it is fully done training, the same waythat a human apprentice may help out someone experienced while theapprentice is learning the job. Entropy can also inform what features orinputs to the robot are useful and which are not.

In some embodiments, process 100 (or 500) may determine (e.g., based ona request 110, 510) the surprisal of one or more data elements (e.g., ofthe manufacturing equipment) or aspects (e.g., features ofcontext-action pairs or aspects of the model) to potentially include inthe manufacturing control computer-based reasoning model. For each ofthe one or more manufacturing or assembly data elements or aspects(collectively called “manufacturing elements”), a first and second PDMFmay be determined 120, 520, 130, 530 (determining the PDMF is describedelsewhere herein). The surprisal for the one or more manufacturingelements may be determined 140, 540 and a determination may be madewhether to select or include 150, 550 the one or more manufacturing dataelements or aspects in the manufacturing control computer-basedreasoning model based on the determined surprisal. While there are moresets of one or more manufacturing data elements or aspects to assess,the process 100, or 500 may return to determine whether moremanufacturing data elements or aspects sets should be included. Oncethere are no more manufacturing data elements or aspects to consider,the process 100 or 500 can turn to controlling 160, 560 themanufacturing system using the manufacturing control computer-basedreasoning system.

Controlling 160, 560 a manufacturing system may be accomplished byprocess 400. For example, if the data elements are related tomanufacturing data elements or aspects, then the manufacturing controlcomputer-based reasoning model trained on that data will controlmanufacturing or assemble. Process 400 proceeds by receiving 410 amanufacturing control computer-based reasoning model. The processproceeds by receiving 420 a context. The manufacturing controlcomputer-based reasoning model is then used to determine 430 an actionto take. The action is then performed by the control system (e.g.,caused by the manufacturing control computer-based reasoning system). Ifthere are more 450 contexts to consider, then the system returns toreceive 420 those contexts and otherwise ceases 460. In suchembodiments, the manufacturing control computer-based reasoning modelmay be used to control a manufacturing system. The chosen actions arethen performed by a control system.

Smart Voice Control

The process 100 may also be applied in the context of smart voicecontrol. For example, combining multiple inputs and forms of analysis,the techniques herein can recognize if there is something unusual abouta voice control request. For example, if a request is to purchase ahigh-priced item or unlock a door, but the calendar and synchronizeddevices indicate that the family is out of town, it could send a requestto the person's phone before confirming the order or action; it could bethat an intruder has recorded someone's voice in the family or has usedartificial intelligence software to create a message and has broken in.It can detect other anomalies for security or for devices activating atunusual times, possibly indicating some mechanical failure, electronicsfailure, or someone in the house using things abnormally (e.g., a childfrequently leaving the refrigerator door open for long durations).Combined with other natural language processing techniques beyondsentiment analysis, such as vocal distress, a smart voice device canrecognize that something is different and ask, improving the person'sexperience and improving the seamlessness of the device into theperson's life, perhaps playing music, adjusting lighting, or HVAC, orother controls. The level of confidence provided by entropy can also beused to train a smart voice device more quickly as it can ask questionsabout aspects of its use that it has the least knowledge about. Forexample: “I noticed usually at night, but also some days, you turn thetemperature down in what situations should I turn the temperature down?What other inputs (features) should I consider?”

Using the techniques herein, a smart voice device may also be able tolearn things it otherwise may not be able to. For example, if the smartvoice device is looking for common patterns in any of the aforementionedactions or purchases and the entropy drops below a certain threshold, itcan ask the person if it should take on a particular action oradditional autonomy without prompting, such as “It looks like you'renormally changing the thermostat to colder on days when you have yourexercise class, but not on days when it is cancelled; should I do thisfrom now on and prepare the temperature to your liking?”

In some embodiments, process 100 or 500 may determine (e.g., based on arequest 110) the surprisal of one or more data elements (e.g., of thesmart voice system) or aspects (e.g., features of the data or parametersof the model) to potentially include in the smart voice system controlcomputer-based reasoning model. For each of the one or more smart voicesystem data elements or aspects, a first and second PDMF may bedetermined 120, 520, 130, 530 (determining the PDMF is describedelsewhere herein). The surprisal for the one or more smart voice systemdata elements or aspects may be determined 140, 540 and a determinationmay be made whether to include 150 the one or more smart voice systemdata elements or aspects in the smart voice system controlcomputer-based reasoning model based on the determined surprisal. Whilethere are more sets of one or more smart voice system data elements oraspects to assess, the process 100 or 500 may return to determinewhether more smart voice system data elements or aspects sets should beincluded. Once there are no more smart voice system data elements oraspects to consider, the process 100 or 500 can turn to controlling 160,560 the smart voice system using the smart voice system controlcomputer-based reasoning model.

Controlling 160, 560 a smart voice system may be accomplished by process400. For example, if the data elements are related to smart voice systemactions, then the smart voice system control computer-based reasoningmodel trained on that data will control smart voice systems. Process 400proceeds by receiving 410 a smart voice computer-based reasoning model.The process proceeds by receiving 420 a context. The smart voicecomputer-based reasoning model is then used to determine 430 an actionto take. The action is then performed by the control system (e.g.,caused by the smart voice computer-based reasoning system). If there aremore 450 contexts to consider, then the system returns to receive 420those contexts and otherwise ceases 460. In such embodiments, the smartvoice computer-based reasoning model may be used to control a smartvoice system. The chosen actions are then performed by a control system.

Control of Federarted Devices

The process 100 or 500 may also be applied in the context of federateddevices in a system. For example, combining multiple inputs and forms ofanalysis, the techniques herein can recognize if there is something thatshould trigger action based on the state of the federated devices. Forexample, if the training data includes actions normally taken and/orstatuses of federated devices, then an action to take could be anoften-taken action in the certain (or related contexts). For example, inthe context of a smart home with interconnected heating, cooling,appliances, lights, locks, etc., the training data could be what aparticular user does at certain times of day and/or in particularsequences. For example, if, in a house, the lights in the kitchen arenormally turned off after the stove has been off for over an hour andthe dishwasher has been started, then when that context again occurs,but the kitchen light has not been turned off, the computer-basedreasoning system may cause an action to be taken in the smart homefederated systems, such as prompting (e.g., audio) whether the user ofthe system would like the kitchen lights to be turned off. As anotherexample, training data may indicate that a user sets the house alarm andlocks the door upon leaving the house (e.g., as detected via goefence).If the user leaves the geofenced location of the house and has not yetlocked the door and/or set the alarm, the computer-based reasoningsystem may cause performance of an action such as inquiring whether itshould lock the door and/or set an alarm. As yet another example, in thesecurity context, the control may be for turning on/off cameras, orenact other security measures, such as sounding alarms, locking doors,or even releasing drones and the like. Training data may includeprevious logs and sensor data, door or window alarm data, time of day,security footage, etc. and when security measure were (or should havebeen) taken. For example, a context such as particular window alarm datafor a particular basement window coupled with other data may beassociated with an action of sounding an alarm, and when a contextoccurs related to that context, an alarm may be sounded.

In some embodiments, process 100 or 500 may determine (e.g., based on arequest 110, 510) the surprisal of one or more data elements or aspectsof the federated device control system for potential inclusion in thefederated device control computer-based reasoning model. For each of theone or more federated device control system data elements or aspects, afirst and second PDMF may be determined 120, 130, 520, 530 (determiningthe PDMF is described elsewhere herein). The surprisal for the one ormore federated device control system data elements may be determined140, 540 and a determination may be made whether to select or include150, 550 the one or more federated device control system data elementsin the federated device control computer-based reasoning model based onthe determined surprisal. While there are more sets of one or morefederated device control system data elements or aspects to assess, theprocess 100 or 500 may return to determine whether more federated devicecontrol system data elements or aspect sets should be included. Oncethere are no more federated device control system data elements oraspects to consider, the process 100 or 500 can turn to controlling 160,560 the federated device control system using the federated devicecontrol computer-based reasoning model.

Controlling 160, 560 a federated device control system may beaccomplished by process 400. For example, if the data elements arerelated to smart voice system actions, then the federated device controlcomputer-based reasoning model trained on that data will controlfederated device control system. Process 400 proceeds by receiving 410 afederated device control computer-based reasoning model. The processproceeds by receiving 420 a context. The federated device controlcomputer-based reasoning model is then used to determine 430 an actionto take. The action is then performed by the control system (e.g.,caused by the federated device control computer-based reasoning system).If there are more 450 contexts to consider, then the system returns toreceive 420 those contexts and otherwise ceases 460. In suchembodiments, the federated device control computer-based reasoning modelmay be used to control federated devices. The chosen actions are thenperformed by a control system.

Control and Automation of Experiments

The process 100 or 500 may also be used in the context of controlsystems for laboratory experiments. For example, many lab experimentstoday, especially in the biological and life sciences, but also inmaterials science and others, yield combinatorial increases, in terms ofnumbers, of possibilities and results. The fields of design ofexperiment, as well as many combinatorial search and explorationtechniques are currently combined with statistical analysis. However,entropy-based techniques such as those herein can be used to guide asearch for knowledge, especially if combined with utility functions.Automated lab experiments may have actuators and may put differentchemicals, samples, or parts in different combinations and put themunder different circumstances. Using entropy to guide the machinesenables them to hone in on learning how the system under study respondsto different scenarios, and, for example, searching areas of greatestuncertainty. Conceptually speaking, when the surprisal is combined witha value function, especially in a multiplicative fashion, then thecombination is a powerful information theoretic take on the classicexploration vs exploitation trade-offs that are made in search processesfrom artificial intelligence to science to engineering. Additionally,such a system can be made to automate experiments where it can predictthe most effective approach, homing in on the best possible, predictableoutcomes for a specific knowledge base. Further, like in the otherembodiments discussed herein, it could indicate (e.g., raise alarms) tohuman operators when the results are anomalous, or even tell whichfeatures being measured are most useful (so that they can beappropriately measured) or when measurements are not sufficient tocharacterize the outcomes. If the system has multiple kinds of sensorsthat have “costs” (e.g., monetary, time, computation, etc.) or cannot beall activated simultaneously, the feature entropies could be used toactivate or deactivate the sensors to reduce costs or improve thedistinguishability of the experimental results.

In some embodiments, process 100 or 500 may determine (e.g., based on arequest 110) the surprisal of one or more data elements or aspects ofthe experiment control system. For each of the one or more experimentcontrol system date elements (or aspects), a first and second PDMF maybe determined 120, 130, 520, 530 (determining the PDMF is describedelsewhere herein). The surprisal for the one or more experiment controlsystem data elements or aspects may be determined 140, 540 and adetermination may be made whether to select or include 150, 550 the oneor more experiment control system data elements or aspects in experimentcontrol computer-based reasoning model based on the determinedsurprisal. While there are more sets of one or more experiment controlsystem data elements or aspects to assess, the process 100 or 500 mayreturn to determine whether more experiment control system data elementsor aspects sets should be included. Once there are no more experimentcontrol system data elements or aspects to consider, the process 100 or500 can turn to controlling 160, 560 the experiment control system usingthe experiment control computer-based reasoning model.

Controlling 160, 560 an experiment control system may be accomplished byprocess 400. For example, if the data elements are related to smartvoice system actions, then the experiment control computer-basedreasoning model trained on that data will control experiment controlsystem. Process 400 proceeds by receiving 410 an experiment controlcomputer-based reasoning model. The process proceeds by receiving 420 acontext. The experiment control computer-based reasoning model is thenused to determine 430 an action to take. The action is then performed bythe control system (e.g., caused by the experiment controlcomputer-based reasoning system). If there are more 450 contexts toconsider, then the system returns to receive 420 those contexts andotherwise ceases 460. In such embodiments, the experiment controlcomputer-based reasoning model may be used to control experiment. Thechosen actions are then performed by a control system.

Control of Energy Transfer Systems

The process 100 or 500 may also be applied in the context of controlsystems for energy transfer. For example, a building may have numerousenergy sources, including solar, wind, grid-based electrical, batteries,on-site generation (e.g., by diesel or gas), etc. and may have manyoperations it can perform, including manufacturing, computation,temperature control, etc. The techniques herein may be used to controlwhen certain types of energy are used and when certain energy consumingprocesses are engaged. For example, on sunny days, roof-mounted solarcells may provide enough low-cost power that grid-based electrical poweris discontinued during a particular time period while costlymanufacturing processes are engaged. On windy, rainy days, the overheadof running solar panels may overshadow the energy provided, but powerpurchased from a wind-generation farm may be cheap, and only essentialenergy consuming manufacturing processes and maintenance processes areperformed.

In some embodiments, process 100 or 500 may determine (e.g., based on arequest 110, 510) the surprisal of one or more data elements or aspectsof the energy transfer system. For each of the one or more energytransfer system data elements or aspects, a first and second PDMF may bedetermined 120, 130, 520, 530 (determining the PDMF is describedelsewhere herein). The surprisal for the one or more energy transfersystem data elements or aspects may be determined 140, 540 and adetermination may be made whether to select or include 150, 550 the oneor more energy transfer system data elements or aspects in energycontrol computer-based reasoning model based on the determinedsurprisal. While there are more sets of one or more energy transfersystem data elements or aspects to assess, the process 100 or 500 mayreturn to determine whether more energy transfer system data elements oraspects should be included. Once there are no more energy transfersystem data elements or aspects to consider, the process 100 or 500 canturn to controlling 160, 560 the energy transfer system using the energycontrol computer-based reasoning model.

Controlling 160, 560 an energy transfer system may be accomplished byprocess 400. For example, if the data elements are related to smartvoice system actions, then the energy control computer-based reasoningmodel trained on that data will control energy transfer system. Process400 proceeds by receiving 410 an energy control computer-based reasoningmodel. The process proceeds by receiving 420 a context. The energycontrol computer-based reasoning model is then used to determine 430 anaction to take. The action is then performed by the control system(e.g., caused by the energy control computer-based reasoning system). Ifthere are more 450 contexts to consider, then the system returns toreceive 420 those contexts and otherwise ceases 460. In suchembodiments, the energy control computer-based reasoning model may beused to control energy. The chosen actions are then performed by acontrol system.

Example Control Hierarchies

In some embodiments, the technique herein may use a control hierarchy tocontrol systems and/or cause actions to be taken (e.g., as part ofcontrolling 160 in FIG. 1). There are numerous example controlhierarchies and many types of systems to control, and hierarchy forvehicle control is presented below. In some embodiments, only a portionof this control hierarchy is used. It is also possible to add levels to(or remove levels from) the control hierarchy.

An example control hierarchy for controlling a vehicle could be:

-   -   Primitive Layer—Active vehicle abilities (accelerate,        decelerate), lateral, elevation, and orientation movements to        control basic vehicle navigation    -   Behavior Layer—Programmed vehicle behaviors which prioritize        received actions and directives and prioritize the behaviors in        the action.    -   Unit Layer—Receives orders from command layer, issues        moves/directives to the behavior layer.    -   Command Layers (hierarchical)—Receives orders and gives orders        to elements under its command, which may be another command        layer or unit layer.

Example Data Elements, Contexts, and Operational Situations

In some embodiments, the data elements may include context data andaction data in context-action pairs. Further, data elements may relateto control of a vehicle. For example, context data may include datarelated to the operation of the vehicle, including the environment inwhich it is operating, and the actions taken may be of any granularity.Consider an example of data collected while a driver, Alicia, drivesaround a city. The collected data could be context and action data wherethe actions taken can include high-level actions (e.g., drive to nextintersection, exit the highway, take surface roads, etc.), mid-levelactions (e.g., turn left, turn right, change lanes) and/or low-levelactions (e.g., accelerate, decelerate, etc.). The contexts can includeany information related to the vehicle (e.g. time until impact withclosest object(s), speed, course heading, breaking distances, vehicleweight, etc.), the driver (pupillary dilation, heart rate,attentiveness, hand position, foot position, etc.), the environment(speed limit and other local rules of the road, weather, visibility,road surface information, both transient such as moisture level as wellas more permanent, such as pavement levelness, existence of potholes,etc.), traffic (congestion, time to a waypoint, time to destination,availability of alternate routes, etc.), and the like. These input data(e.g., context-action pairs for training a context-based reasoningsystem or input training contexts with outcome actions for training amachine learning system) can be saved and later used to help control acompatible vehicle in a compatible operational situation. Theoperational situation of the vehicle may include any relevant datarelated to the operation of the vehicle. In some embodiments, theoperational situation may relate to operation of vehicles by particularindividuals, in particular geographies, at particular times, and inparticular conditions. For example, the operational situation may referto a particular driver (e.g., Alicia or Carole). Alicia may beconsidered a cautious car driver, and Carole a faster driver. As notedabove, and in particular, when approaching a stop sign, Carole may coastin and then brake at the last moment, while Alicia may slow down earlierand roll in. As another example of an operational situation, Bob may beconsidered the “best pilot” for a fleet of helicopters, and thereforehis context and actions may be used for controlling self-flyinghelicopters.

In some embodiments, the operational situation may relate to the localein which the vehicle is operating. The locale may be a geographic areaof any size or type, and may be determined by systems that utilizemachine learning. For example, an operational situation may be “highwaydriving” while another is “side street driving”. An operationalsituation may be related to an area, neighborhood, city, region, state,country, etc. For example, one operational situation may relate todriving in Raleigh, N.C. and another may be driving in Pittsburgh, Pa.An operational situation may relate to safe or legal driving speeds. Forexample, one operational situation may be related to roads withforty-five miles per hour speed limits, and another may relate to turnswith a recommended speed of 20 miles per hour. The operational situationmay also include aspects of the environment such as road congestion,weather or road conditions, time of day, etc. The operational situationmay also include passenger information, such as whether to hurry (e.g.,drive faster), whether to drive smoothly, technique for approaching stopsigns, red lights, other objects, what relative velocity to take turns,etc. The operational situation may also include cargo information, suchas weight, hazardousness, value, fragility of the cargo, temperaturesensitivity, handling instructions, etc.

In some embodiments, the context and action may include vehiclemaintenance information. The context may include information for timingand/or wear-related information for individual or sets of components.For example, the context may include information on the timing anddistance since the last change of each fluid, each belt, each tire (andpossibly when each was rotated), the electrical system, interior andexterior materials (such as exterior paint, interior cushions, passengerentertainment systems, etc.), communication systems, sensors (such asspeed sensors, tire pressure monitors, fuel gauges, compasses, globalpositioning systems (GPS), RADARs, LiDARs, cameras, barometers, thermalsensors, accelerometers, strain gauges, noise/sound measurement systems,etc.), the engine(s), structural components of the vehicle (wings,blades, struts, shocks, frame, hull, etc.), and the like. The actiontaken may include inspection, preventative maintenance, and/or a failureof any of these components. As discussed elsewhere herein, havingcontext and actions related to maintenance may allow the techniques topredict when issues will occur with future vehicles and/or suggestmaintenance. For example, the context of an automobile may include thedistance traveled since the timing belt was last replaced. The actionassociated with the context may include inspection, preventativereplacement, and/or failure of the timing belt. Further, as describedelsewhere herein, the contexts and actions may be collected for multipleoperators and/or vehicles. As such, the timing of inspection,preventative maintenance and/or failure for multiple automobiles may bedetermined and later used for predictions and messaging.

Causing performance of an identified action can include sending a signalto a real car, to a simulator of a car, to a system or device incommunication with either, etc. Further, the action to be caused can besimulated/predicted without showing graphics, etc. For example, thetechniques might cause performance of actions in the manner thatincludes, determining what action would be take, and determining whetherthat result would be anomalous, and performing the techniques hereinbased on the determination that such state would be anomalous based onthat determination, all without actually generating the graphics andother characteristics needed for displaying the results needed in agraphical simulator (e.g., a graphical simulator might be similar to acomputer game).

Example Systems for Entropy-Based Techniques For Creation ofWell-Balanced Computer Based Reasoning Systems

FIG. 2 depicts a block diagram of a system for evolving computer-basedreasoning systems. System 200 includes a number of elements connected bya communicative coupling or network 290. Examples of communicativecoupling and networks are described elsewhere herein. In someembodiments, the process 100 of FIG. 1 may run on the system 200 of FIG.2 and/or the hardware 300 of FIG. 3. For example, the receiving 110 anddetermining 120-150 of FIG. 1 may be handled at training and analysissystem 210. The resultant set(s) of data elements might be stored incommunicatively coupled storage 230 or 240. The control system 220 maycontrol 160 one or more physical systems.

Each of training and analysis system 210 and control system 220 may runon a single computing device, multiple computing devices, in adistributed manner across a network, on one or more virtual machines,which themselves run on one or more computing devices. In someembodiments, training and analysis system 210 and control system 220 aredistinct sets of processes running on distinct sets of computingdevices. In other embodiments, training and analysis system 210 andcontrol system 220 are intertwined or share processes or functionsand/or run on the same computing devices. In some embodiments, storage230 and 240 are communicatively coupled to training and analysis system210 and control system 220 via a network 290 or other connection.Storage 230 and 240 may also be part of or integrated with training andanalysis system 210 and/or control system 220 via a network 290 or otherconnection.

As discussed herein the various aspects or embodiments of process 100may run in parallel, in conjunction, together, or one process may be asubprocess of another. Further, any of the processes may run on thesystems or hardware discussed herein.

Hardware Overview

According to some embodiments, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 3 is a block diagram that illustrates a computersystem 300 upon which an embodiment of the invention may be implemented.Computer system 300 includes a bus 302 or other communication mechanismfor communicating information, and a hardware processor 304 coupled withbus 302 for processing information. Hardware processor 304 may be, forexample, a general purpose microprocessor.

Computer system 300 also includes a main memory 306, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 302for storing information and instructions to be executed by processor304. Main memory 306 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 304. Such instructions, when stored innon-transitory storage media accessible to processor 304, rendercomputer system 300 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 300 further includes a read only memory (ROM) 308 orother static storage device coupled to bus 302 for storing staticinformation and instructions for processor 304. A storage device 310,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 302 for storing information and instructions.

Computer system 300 may be coupled via bus 302 to a display 312, such asan OLED, LED or cathode ray tube (CRT), for displaying information to acomputer user. An input device 314, including alphanumeric and otherkeys, is coupled to bus 302 for communicating information and commandselections to processor 304. Another type of user input device is cursorcontrol 316, such as a mouse, a trackball, or cursor direction keys forcommunicating direction information and command selections to processor304 and for controlling cursor movement on display 312. This inputdevice typically has two degrees of freedom in two axes, a first axis(e.g., x) and a second axis (e.g., y), that allows the device to specifypositions in a plane. The input device 314 may also have multiple inputmodalities, such as multiple 2-axes controllers, and/or input buttons orkeyboard. This allows a user to input along more than two dimensionssimultaneously and/or control the input of more than one type of action.

Computer system 300 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 300 to be a special-purpose machine. Accordingto some embodiments, the techniques herein are performed by computersystem 300 in response to processor 304 executing one or more sequencesof one or more instructions contained in main memory 306. Suchinstructions may be read into main memory 306 from another storagemedium, such as storage device 310. Execution of the sequences ofinstructions contained in main memory 306 causes processor 304 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 310. Volatile media includes dynamic memory, such asmain memory 306. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 302. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 304 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 300 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 302. Bus 302 carries the data tomain memory 306, from which processor 304 retrieves and executes theinstructions. The instructions received by main memory 306 mayoptionally be stored on storage device 310 either before or afterexecution by processor 304.

Computer system 300 also includes a communication interface 318 coupledto bus 302. Communication interface 318 provides a two-way datacommunication coupling to a network link 320 that is connected to alocal network 322. For example, communication interface 318 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 318 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 318sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.Such a wireless link could be a Bluetooth, Bluetooth Low Energy (BLE),802.11 WiFi connection, or the like.

Network link 320 typically provides data communication through one ormore networks to other data devices. For example, network link 320 mayprovide a connection through local network 322 to a host computer 324 orto data equipment operated by an Internet Service Provider (ISP) 326.ISP 326 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 328. Local network 322 and Internet 328 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 320and through communication interface 318, which carry the digital data toand from computer system 300, are example forms of transmission media.

Computer system 300 can send messages and receive data, includingprogram code, through the network(s), network link 320 and communicationinterface 318. In the Internet example, a server 330 might transmit arequested code for an application program through Internet 328, ISP 326,local network 322 and communication interface 318.

The received code may be executed by processor 304 as it is received,and/or stored in storage device 310, or other non-volatile storage forlater execution.

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense. The sole and exclusive indicator of the scope of the invention,and what is intended by the applicants to be the scope of the invention,is the literal and equivalent scope of the set of claims that issue fromthis application, in the specific form in which such claims issue,including any subsequent correction.

What is claimed is:
 1. A method comprising: receiving, at a training andanalysis system, a request to determine whether to include one or moreparticular data elements in a computer-based reasoning model, whereinthe training and analysis system executes on one or more computingdevices, and is configured to execute training and analysisinstructions; determining, at the training and analysis system, a firstPDMF for a set of data elements that does not include the one or moreparticular data elements, wherein the set of data elements is associatedwith the computer-based reasoning model; determining, at the trainingand analysis system, a second PDMF for the set of data elements combinedwith the one or more particular data elements; determining, at thetraining and analysis system, surprisal of the one or more particulardata elements based on a ratio of the first PDMF and the second PDMF; inresponse to determining that the surprisal of the one or more particulardata elements is out of bounds of one or more predetermined thresholds,including the one or more particular data elements in the computer-basedreasoning model; in response to determining that the surprisal of theone or more particular data elements is not out of bounds of the one ormore predetermined thresholds, excluding the one or more particular dataelements from the computer-based reasoning model; causing, with acontrol system, control of a system with the computer-based reasoningmodel, wherein the method is performed on one or more computing devices.2. The method of claim 1, further comprising: wherein the one or moreparticular data elements includes at least one label on at least onetraining image; wherein causing control of the system comprises causingcontrol of a system that identifies elements of an image using thecomputer-based reasoning model by: receiving an input image forlabelling; determining one or more labels for the input image based onthe image and the computer-based reasoning model; labelling the inputimage based on the one or more determined labels.
 3. The method of claim1, further comprising: wherein causing control of the system comprisescausing control of a vehicle using the computer-based reasoning modelby: receiving a current context for the vehicle, wherein the vehicle canbe controlled by the control system; determining an action to take forthe vehicle based on the current context for the vehicle and thecomputer-based reasoning model; causing the vehicle to perform thedetermined action.
 4. The method of claim 1, wherein the one or moreparticular data elements is a context-action pair and the set of dataelements is a set of context action pairs and the first and second PDMFsare measures of probability density for context-action pairs.
 5. Themethod of claim 1, further comprising: receiving a request for modelreduction of the computer-based reasoning model; determining surprisalof each context-action pair in a full set of context-action pairsrelative to the computer-based reasoning model without it, the full setof context-action pairs including the one or more particular dataelements and the set of data elements; determining a subset of the fullset of context-action pairs based at least in part on the surprisal ofeach context-action pair in the full set of context-action pairs,wherein the subset of the full set of context-action pairs contains onlycontext-action pairs from the full set of context-action pairs for whichsurprisal is not out of bounds of the one or more predeterminedthresholds; and responding to the request for reduction of the with thesubset of the full set of context-action pairs.
 6. The method of claim1, further comprising: initially receiving the one or more particulardata elements as part of training for the computer-based reasoningmodel; in response to determining that the surprisal of the one or moreparticular data elements is above a predetermined threshold, sending anindication to a trainer associated with the training for thecomputer-based reasoning model to continue to train related to the oneor more particular data elements; in response to determining that thesurprisal of the one or more particular data elements is not above apredetermined threshold, sending the indication to the trainerassociated with the training for the computer-based reasoning model thattraining is no longer needed related to the one or more particular dataelements.
 7. The method of claim 1, further comprising: receiving arequest for an action to take in a current context; determining theaction to take based on comparing the current context to contextsassociated with data elements in the set of data elements; andresponding to the request for the action to take with the determinedaction.
 8. The method of claim 7, further comprising: receiving anindication that there was an error associated with the determinedaction; removing a data element associated with the determined actionfrom the set of data elements.
 9. The method of claim 7, furthercomprising: receiving an indication that there was an error associatedwith the determined action; adding, to the set of data elements, one ormore additional data elements with contexts associated with the currentcontext, wherein the one or more additional data elements would cause adetermination that the current context is associated with one of the oneor more additional data elements, and would cause determination that thecurrent context would be associated with a different action than anaction associated with the error.
 10. The method of claim 1, furthercomprising determining the first PDMF using a parametric distribution.11. The method of claim 1, further comprising determining the first PDMFusing a nonparametric distribution.
 12. The method of claim 1, furthercomprising: determining multiple nearest data elements from the set ofdata elements for the one or more particular data elements; determiningmultiple premetric contributions, one for each of the multiple nearestdata elements; determining a premetric measurement of the one or moreparticular data elements based at least in part on the multiplepremetric contributions; and determining new premetric measurements forat least one data element in the set of data elements, wherein each newpremetric measurement for the at least one data element is computedbased on premetric measurement to the one or more particular dataelements; determining the second PDMF based at least in part on thepremetric measurement for the one or more particular data elements andthe new premetric measurements for the at least one data element in theset of data elements.
 13. The method of claim 12, wherein: the firstPDMF is computed based on an average premetric contribution of each dataelement in the set of data elements divided by a sum of premetriccontributions of each data element in the set of data elements; andfurther comprising: determining the second PDMF based on the premetricmeasurement of the one or more particular data elements divided by a sumof the new premetric measurements.
 14. A non-transitory computerreadable medium storing instructions which, when executed by one or morecomputing devices, cause the one or more computing devices to performthe method of claim
 1. 15. A system for creating a computer-basedreasoning model, comprising: a training and analysis system executing onone or more computing devices, and configured to execute training andanalysis instructions, which, when executed, perform the steps of:receiving a request to determine whether to include one or moreparticular data elements in a computer-based reasoning model;determining a first PDMF for a set of data elements that does notinclude the one or more particular data elements, wherein the set ofdata elements is associated with the computer-based reasoning model;determining a second PDMF for the set of data elements combined with theone or more particular data elements; determining surprisal of the oneor more particular data elements based on a ratio of the first PDMF andthe second PDMF; in response to determining that the surprisal of theone or more particular data elements is out of bounds of one or morepredetermined thresholds, including the one or more particular dataelements in the computer-based reasoning model; in response todetermining that the surprisal of the one or more particular dataelements is not out of bounds of the one or more predeterminedthresholds, excluding the one or more particular data elements from thecomputer-based reasoning model; sending the computer-based reasoningmodel to a control system; a control system executing on the one or morecomputing devices, configured to communicate with the training andanalysis system and to execute control system instructions, which, whenexecuted, perform the steps of: receiving the computer-based reasoningmodel from the training and analysis system; receiving a current contextfor a target system, wherein the target system can be controlled by thecontrol system; determining an action to take for the target systembased on the current context for the target system and thecomputer-based reasoning model; causing the target system to perform thedetermined action.
 16. The system of claim 15, the training and analysissystem further configured to perform the steps of: determining multiplenearest data elements from the set of data elements for the one or moreparticular data elements; determining multiple premetric contributions,one for each of the multiple nearest data elements; determining apremetric measurement of the one or more particular data elements basedat least in part on the multiple premetric contributions; anddetermining new premetric measurements for at least one data element inthe set of data elements, wherein each new premetric measurement for theat least one data element is computed based on premetric measurement tothe one or more particular data elements; determining the second PDMFbased at least in part on the premetric measurement for the one or moreparticular data elements and the new premetric measurements for the atleast one data element in the set of data elements.
 17. The system ofclaim 16, wherein: the first PDMF is computed based on an averagepremetric contribution of each data element in the set of data elementsdivided by a sum of premetric contributions of each data element in theset of data elements; and further comprising: determining the secondPDMF based on the premetric measurement of the one or more particulardata elements divided by a sum of the new premetric measurements.
 18. Asystem for creating a computer-based reasoning model for controllingvehicles, comprising: a vehicle training and analysis system executingon one or more computing devices, and configured to execute training andanalysis instructions, which, when executed, perform the steps of:receiving a request to determine whether to include one or moreparticular vehicular data elements in a vehicular computer-basedreasoning model; determining a first PDMF for a set of vehicular dataelements that does not include the one or more particular vehicular dataelements, wherein the set of vehicular data elements is associated withthe vehicular computer-based reasoning model; determining a second PDMFfor the set of vehicular data elements combined with the one or moreparticular vehicular data elements, determining surprisal of the one ormore particular vehicular data elements based on a ratio of the firstPDMF and the second PDMF; in response to determining that the surprisalof the one or more particular vehicular data elements is out of boundsof one or more predetermined thresholds, including the one or moreparticular vehicular data elements in the computer-based reasoningmodel; in response to determining that the surprisal of the one or moreparticular vehicular data elements is not out of bounds of the one ormore predetermined thresholds, excluding the one or more particularvehicular data elements from the vehicular computer-based reasoningmodel; sending the vehicular computer-based reasoning model to a controlsystem; a control system executing on the one or more computing devices,configured to communicate with the vehicle training and analysis systemand to execute control system instructions, which, when executed,perform the steps of: receiving the vehicular computer-based reasoningmodel from the vehicle training and analysis system; receiving a currentcontext for a vehicle, control of which can be performed by the controlsystem; determining an action to take for the vehicle based on thecurrent context for the vehicle and the vehicular computer-basedreasoning model; causing the vehicle to perform the determined action.19. The system of claim 18, the vehicle training and analysis systemfurther configured to perform the steps of: determining multiple nearestvehicular data elements from the set of vehicular data elements for theone or more particular vehicular data elements; determining multiplepremetric contributions, one for each of the multiple nearest vehiculardata elements; determining a premetric measurement of the one or moreparticular vehicular data elements based at least in part on themultiple premetric contributions; and determining new premetricmeasurements for at least one vehicular data element in the set ofvehicular data elements, wherein each new premetric measurement for theat least one vehicular data element is computed based on premetricmeasurement to the one or more particular vehicular data elements;determining the second PDMF based at least in part on the premetricmeasurement for the one or more particular vehicular data elements andthe new premetric measurements for the at least one vehicular dataelement in the set of vehicular data elements.
 20. The system of claim19, wherein: the first PDMF is computed based on an average premetriccontribution of each vehicular data element in the set of vehicular dataelements divided by a sum of premetric contributions of each vehiculardata element in the set of vehicular data elements; and furthercomprising: determining the second PDMF based on the premetricmeasurement of the one or more particular vehicular data elementsdivided by a sum of the new premetric measurements.